Show EDF problem with Emotiv data.

gistfile1.py

import logging, argparse, itertools

import numpy as np
import matplotlib.pyplot as plt
from scipy import signal

import eegtools

log = logging.getLogger(__name__)


if __name__ == '__main__':
  parser = argparse.ArgumentParser(description='Assess signal quality')
  parser.add_argument('--subject', help='The subject ID.', default='1a-01')
  args = parser.parse_args()

  logging.basicConfig(level=logging.INFO)
  log.setLevel(logging.INFO)
  plt.ion()
  
  d = eegtools.data.hmi_wow.load(args.subject)

  # Perform filtering
  (b, a) = signal.butter(2, np.array([.5, 40]) / (d.sample_rate / 2), 'band')
  X = signal.lfilter(b, a, d.X, axis=1)

  # Plot raw EEG
  plt.figure(); plt.title('Filtered EEG')
  win = slice(100 * d.sample_rate, 105 * d.sample_rate)
  plt.plot(X[:,win].T + np.arange(X.shape[0]) * 60, c='k')
  plt.ylabel('amplitude (mV)')
  plt.xlabel('time (samples @ %dHz) ' % d.sample_rate)

  # Analyze frequency spectrum.
  plt.figure()
  plt.psd(X[0], Fs=d.sample_rate)
  plt.title('Power spectrum')
  
  # Analyze covariance.
  C = np.cov(X)
  plt.matshow(C); plt.colorbar(); plt.title('Channel covariance')

  plt.figure(); plt.title('Spectrum of channel covariance.')
  plt.semilogy(np.sort(np.linalg.eigvalsh(C)))